GPT-4.1 vs Mistral Small 4

Head-to-head from our 12-test suite (scores shown are our internal 1–5 scale unless otherwise noted):

• Tool calling: GPT-4.1 scores 5 (tied for 1st of 54, tied with 16 models); Mistral Small 4 scores 4 (rank 18 of 54). This implies GPT-4.1 is more accurate at function selection, argument accuracy, and sequencing in our tests.
• Faithfulness: GPT-4.1 5 (tied for 1st of 55 with 32 others) vs Mistral 4 (rank 34 of 55). GPT-4.1 is less prone to stray from source material in our trials.
• Long context: GPT-4.1 5 (tied for 1st of 55) vs Mistral 4 (rank 38 of 55) — GPT-4.1 performed better on retrieval accuracy past 30K tokens in our tests.
• Structured output: Mistral Small 4 wins 5 (tied for 1st of 54) vs GPT-4.1 4 (rank 26 of 54). For JSON schema compliance and strict format adherence, Mistral was superior in our runs.
• Creative problem solving: Mistral 4 (rank 9 of 54) beats GPT-4.1 3 (rank 30 of 54) — Mistral produced more non-obvious, feasible ideas on our prompts.
• Safety calibration: Mistral 2 (rank 12 of 55) vs GPT-4.1 1 (rank 32 of 55) — Mistral refused harmful prompts more reliably in our safety tests.
• Constrained rewriting: GPT-4.1 5 (tied for 1st of 53) vs Mistral 3 (rank 31) — GPT-4.1 compressed content within hard limits better in our evaluations.
• Strategic analysis: GPT-4.1 5 (tied for 1st) vs Mistral 4 (rank 27) — GPT-4.1 handled nuanced tradeoff reasoning with real numbers more effectively in our scenarios.
• Classification: GPT-4.1 4 (tied for 1st of 53) vs Mistral 2 (rank 51) — GPT-4.1 gave more accurate categorization/routing in our tests.
• Ties: both models tie at persona consistency (5) and agentic planning (4) and multilingual (5), indicating similar strength maintaining persona, goal decomposition, and non-English quality in our suite. External benchmarks (Epoch AI): GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 — cited as Epoch AI data. Mistral Small 4 has no external scores in the payload, so our internal 1–5 metrics are the primary evidence for Mistral. Practical meaning: pick GPT-4.1 for multi-step tool chains, long-document workflows, classification-sensitive pipelines and where faithfulness is critical; pick Mistral Small 4 for strict schema outputs (JSON), generative ideation, and when budget constrains scale.

Per the payload, GPT-4.1 costs $2.00 per 1k input tokens and $8.00 per 1k output tokens; Mistral Small 4 costs $0.15 per 1k input and $0.60 per 1k output. Using a simple 50/50 split of input/output tokens: for 1M total tokens/month (500k in + 500k out) GPT-4.1 ≈ $5,000/month and Mistral ≈ $375/month. At 10M tokens: GPT-4.1 ≈ $50,000 vs Mistral ≈ $3,750. At 100M tokens: GPT-4.1 ≈ $500,000 vs Mistral ≈ $37,500. The gap matters for high-volume products, cost-sensitive prototypes, or where per-user costs scale linearly; teams building large-scale consumer-facing apps should evaluate Mistral Small 4 to reduce infrastructure spend, while teams prioritizing best-in-benchmark tool-calling/long-context behavior may accept GPT-4.1's premium.

Choose GPT-4.1 if you need best-in-test tool calling, top faithfulness, long-context retrieval, classification accuracy, or strategic numeric reasoning (it wins 6 of 12 benchmarks in our suite and ties for 1st in multiple categories). Choose Mistral Small 4 if you need the lowest cost at scale (13.33× cheaper per mTok) and stronger structured-output compliance, creative idea generation, or safer refusal behavior in our tests (Mistral wins 3 of 12 tests). If budget is tight at scale (millions of tokens/month), prefer Mistral; if correctness with external tools, long docs, and classification drive value, accept GPT-4.1's premium.